Method and Device for the Iterative Reconstruction of Cardiac Images

1. A data processing unit for an iterative reconstruction of cross-sectional images of an object in a given observation phase of a cyclic movement of the object, comprising: an input module for reception of projections of the object from different directions and of simultaneously measured movement parameters characterizing the cyclic movement of the object; a processing module for determining a time distance from a projection to the observation phase for one or more of the projections; a sorting module for sorting the projections into subsets such that the projections within a subset correspond to a predetermined time distance range to the observation phase; and a reconstruction module for the iterative reconstruction of a cross-sectional image with an algorithm that processes in each iteration all projections of at least one subset.

2. The data processing unit according to claim 1 , wherein the iterative reconstruction weights projections with an aperture function.

3. The data processing unit according to claim 1 , wherein the projections are X-ray projections.

4. The data processing unit according to claim 1 , further including a weighting module for associating a weight to the movement parameters that measures the similarity of the movement parameters to the observation phase.

5. The data processing unit according to claim 1 , wherein the subsets are approximately of equal size.

6. The data processing unit according to claim 1 , wherein projections corresponding to similar time distance ranges are distributed randomly among the associated subsets.

7. The data processing unit according to claim 1 , wherein the iterative reconstruction is based on an ART or ML algorithm, wherein an image update in each iteration uses all projections of a selected subset.

8. The data processing unit according to claim 1 , wherein the projections are weighted with a weight to determine the time distance.

9. The data processing unit according to claim 8 , wherein the weighting is applied to the projections with a weighting function.

10. The data processing unit according to claim 9 , wherein the weighting function is variable and includes a peak of a predetermined width centered at the observation phase.

11. An examination apparatus, comprising a rotational X-ray device; an electrocardiographic device; and a data processing unit according to claim 1 that is coupled to the X-ray device and the electrocardiographic device.

12. A method for the iterative reconstruction of cross-sectional images of an object in a given observation phase of a cyclic movement of the object based on projections of the object from different directions and on simultaneous measurements of movement parameters characterizing the associated cyclic movement of the object, comprising: determining a time distance from a projection to the observation base for one or more of the projections; sorting the projections into subsets such that the projections within a subset correspond to a redetermined time distance range to the observation phase; and iteratively reconstructing a cross-sectional image from the projections, wherein in an iteration all projections of at least one subset are processed.

13. A computer readable medium on which a computer program for the iterative reconstruction of cross-sectional images of the object in the given observation phase of its cyclic movement is stored, said computer program being adapted to execute the method according to claim 12 .

14. The method according to claim 12 , wherein the iterative reconstruction further includes weighting projections with an aperture function.

15. The method according to claim 12 , further including applying a weighting to the projections to determine the time distance.

16. The method according to claim 15 , further including applying the weighting to the projections with a weighting function.

17. The method according to claim 16 , wherein the weighting function is variable and includes a peak of a predetermined width centered at the observation phase.

18. The method according to claim 12 , further including weighting the movement parameters with a weighting module that measures the similarity of the movement parameters to the observation phase.

19. The method according to claim 12 , further including sorting the subsets such that the subsets are filled with approximately a same number of projections.

20. The method according to claim 12 , further including distributing projections corresponding to similar time distance ranges randomly among the associated subsets.